## pval_cutoff: 0.05
## lfc_cutoff: 1
## low_counts_cutoff: 10
General statistics
# Number of samples
length(counts_data)
## [1] 6
# Number of genes
nrow(counts_data)
## [1] 55487
# Total counts
colSums(counts_data)
## SRR13535276 SRR13535278 SRR13535280 SRR13535300 SRR13535302 SRR13535304
## 3107284 2321609 3701956 2491487 1580539 1861995
Create DDS objects
# Create DESeqDataSet object
dds <- get_DESeqDataSet_obj(counts_data, ~ treatment)
## [1] TRUE
## [1] TRUE
## [1] "DESeqDataSet object of length 55487 with 0 metadata columns"
## [1] "DESeqDataSet object of length 14648 with 0 metadata columns"
colData(dds)
## DataFrame with 6 rows and 25 columns
## Assay Type AvgSpotLen Bases BioProject BioSample Bytes Center Name Consent DATASTORE filetype DATASTORE provider DATASTORE region Experiment treatment GEO_Accession (exp) Instrument LibraryLayout LibrarySelection LibrarySource Organism Platform label ReleaseDate Sample Name source_name SRA Study
## <character> <numeric> <numeric> <character> <character> <numeric> <character> <character> <character> <character> <character> <character> <factor> <character> <character> <character> <character> <character> <character> <character> <factor> <POSIXct> <character> <character> <character>
## SRR13535276 RNA-Seq 300 8225466000 PRJNA694971 SAMN17588686 3252113587 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.us-east-1 SRX9943360 A GSM5043430 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA in space without gravity 2021-09-09 GSM5043430 C2C12 proliferating myoblasts SRP303354
## SRR13535278 RNA-Seq 300 9203426700 PRJNA694971 SAMN17588684 3619152333 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.us-east-1 SRX9943362 A GSM5043433 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA in space without gravity 2021-09-09 GSM5043433 C2C12 proliferating myoblasts SRP303354
## SRR13535280 RNA-Seq 300 9323939700 PRJNA694971 SAMN17588682 3735905901 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.us-east-1 SRX9943364 A GSM5043436 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA in space without gravity 2021-09-09 GSM5043436 C2C12 proliferating myoblasts SRP303354
## SRR13535300 RNA-Seq 300 12820015200 PRJNA694971 SAMN17587361 5047533646 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.us-east-1 SRX9943384 E GSM5043471 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA on land 2021-09-09 GSM5043471 C2C12 proliferating myoblasts SRP303354
## SRR13535302 RNA-Seq 300 12499917600 PRJNA694971 SAMN17587359 4941074444 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.us-east-1 SRX9943386 E GSM5043475 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA on land 2021-09-09 GSM5043475 C2C12 proliferating myoblasts SRP303354
## SRR13535304 RNA-Seq 300 7150086300 PRJNA694971 SAMN17587357 2845819297 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.us-east-1 SRX9943388 E GSM5043478 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA on land 2021-09-09 GSM5043478 C2C12 proliferating myoblasts SRP303354
Sample-to-sample comparisons
# Transform data (blinded rlog)
rld <- get_transformed_data(dds)
PCA plot
pca <- rld$pca
pca_df <- cbind(as.data.frame(colData(dds)) %>% rownames_to_column(var = 'name'), pca$x)
summary(pca)
## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6
## Standard deviation 40.9653 35.5987 26.4305 19.10170 17.10922 1.017e-13
## Proportion of Variance 0.3901 0.2946 0.1624 0.08482 0.06805 0.000e+00
## Cumulative Proportion 0.3901 0.6847 0.8471 0.93195 1.00000 1.000e+00
ggplot(pca_df, aes(x = PC1, y = PC2, color = label)) +
geom_point() +
geom_text(aes(label = name), position = position_nudge(y = -2), show.legend = F, size = 3) +
scale_color_manual(values = colors_default) +
scale_x_continuous(expand = c(0.2, 0))
Correlation heatmap
pheatmap(
cor(rld$matrix),
annotation_col = as.data.frame(colData(dds)) %>% select(label),
color = brewer.pal(8, 'YlOrRd')
)
Wald test results
# DE analysis using Wald test
dds_full <- DESeq(dds)
colData(dds_full)
## DataFrame with 6 rows and 26 columns
## Assay Type AvgSpotLen Bases BioProject BioSample Bytes Center Name Consent DATASTORE filetype DATASTORE provider DATASTORE region Experiment treatment GEO_Accession (exp) Instrument LibraryLayout LibrarySelection LibrarySource Organism Platform label ReleaseDate Sample Name source_name SRA Study sizeFactor
## <character> <numeric> <numeric> <character> <character> <numeric> <character> <character> <character> <character> <character> <character> <factor> <character> <character> <character> <character> <character> <character> <character> <factor> <POSIXct> <character> <character> <character> <numeric>
## SRR13535276 RNA-Seq 300 8225466000 PRJNA694971 SAMN17588686 3252113587 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.us-east-1 SRX9943360 A GSM5043430 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA in space without gravity 2021-09-09 GSM5043430 C2C12 proliferating myoblasts SRP303354 0.970042620530961
## SRR13535278 RNA-Seq 300 9203426700 PRJNA694971 SAMN17588684 3619152333 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.us-east-1 SRX9943362 A GSM5043433 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA in space without gravity 2021-09-09 GSM5043433 C2C12 proliferating myoblasts SRP303354 1.33983479284256
## SRR13535280 RNA-Seq 300 9323939700 PRJNA694971 SAMN17588682 3735905901 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.us-east-1 SRX9943364 A GSM5043436 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA in space without gravity 2021-09-09 GSM5043436 C2C12 proliferating myoblasts SRP303354 1.08830437727923
## SRR13535300 RNA-Seq 300 12820015200 PRJNA694971 SAMN17587361 5047533646 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.us-east-1 SRX9943384 E GSM5043471 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA on land 2021-09-09 GSM5043471 C2C12 proliferating myoblasts SRP303354 1.43563336306498
## SRR13535302 RNA-Seq 300 12499917600 PRJNA694971 SAMN17587359 4941074444 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.us-east-1 SRX9943386 E GSM5043475 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA on land 2021-09-09 GSM5043475 C2C12 proliferating myoblasts SRP303354 0.769364687058128
## SRR13535304 RNA-Seq 300 7150086300 PRJNA694971 SAMN17587357 2845819297 GEO public fastq,sra gs,ncbi,s3 gs.US,ncbi.public,s3.us-east-1 SRX9943388 E GSM5043478 Illumina HiSeq 2500 PAIRED cDNA TRANSCRIPTOMIC Mus musculus ILLUMINA on land 2021-09-09 GSM5043478 C2C12 proliferating myoblasts SRP303354 0.596304865135331
# Wald test results
res <- results(
dds_full,
contrast = c('treatment', condition, control),
alpha = pval_cutoff
)
res
## log2 fold change (MLE): treatment A vs E
## Wald test p-value: treatment A vs E
## DataFrame with 14648 rows and 6 columns
## baseMean log2FoldChange lfcSE stat pvalue padj
## <numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
## ENSMUSG00000025900 4.914147077455 -4.92038939858524 2.01247166836637 -2.44494840644359 0.0144872864884619 NA
## ENSMUSG00000098104 4.09533781074173 0.853116865234774 1.10703456347057 0.770632546973277 0.440924763843958 NA
## ENSMUSG00000033845 107.622165011027 -0.107664037289825 0.423913828571251 -0.253976232982758 0.799513919532903 0.928640368582514
## ENSMUSG00000102275 2.36352235488834 -0.391339522802433 1.46485014416748 -0.267153281419679 0.789351145202682 NA
## ENSMUSG00000025903 97.3741809067814 -0.000670710765897874 0.485955321775427 -0.00138019018589496 0.99889876790933 0.999586418726237
## ... ... ... ... ... ... ...
## ENSMUSG00000061654 1.69274896880504 1.42421218490796 2.57607432131434 0.552861450123578 0.58035828636321 NA
## ENSMUSG00000079834 28.8069677321529 0.998795513949071 0.80844927148458 1.23544611786829 0.216664518073648 0.549180345002018
## ENSMUSG00000095041 184.206277782681 0.0979395592239719 0.573810611348214 0.170682725775766 0.864473246335269 0.954515887544127
## ENSMUSG00000063897 31.5444997848201 -0.20997393171239 0.718744861367948 -0.292139732745717 0.770179788725231 0.915940742518338
## ENSMUSG00000095742 10.1107048409608 0.14999961155862 0.948601163242324 0.158127163839775 0.874356596236297 0.957516437938579
mcols(res)
## DataFrame with 6 rows and 2 columns
## type description
## <character> <character>
## baseMean intermediate mean of normalized counts for all samples
## log2FoldChange results log2 fold change (MLE): treatment A vs E
## lfcSE results standard error: treatment A vs E
## stat results Wald statistic: treatment A vs E
## pvalue results Wald test p-value: treatment A vs E
## padj results BH adjusted p-values
summary(res)
##
## out of 14648 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0 (up) : 312, 2.1%
## LFC < 0 (down) : 184, 1.3%
## outliers [1] : 179, 1.2%
## low counts [2] : 2840, 19%
## (mean count < 5)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
plotDispEsts(dds_full)
Summary details
# Upregulated genes (LFC > 0)
res_sig_df %>% filter(log2FoldChange > 0)
# Downregulated genes (LFC < 0)
res_sig_df %>% filter(log2FoldChange < 0)
# Outliers (pvalue and padj are NA)
res[which(is.na(res$pvalue)), ]
## log2 fold change (MLE): treatment A vs E
## Wald test p-value: treatment A vs E
## DataFrame with 179 rows and 6 columns
## baseMean log2FoldChange lfcSE stat pvalue padj
## <numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
## ENSMUSG00000067780 318.83564782775 -2.3706959444743 1.42869895255581 -1.65933903726418 NA NA
## ENSMUSG00000025981 152.077989616933 -0.293486203907577 1.15037203790121 -0.25512285959508 NA NA
## ENSMUSG00000038349 100.799409633029 -3.94260165031521 1.21130442878514 -3.2548396229917 NA NA
## ENSMUSG00000026024 50.6697012550001 -3.51747879092895 1.25525049232079 -2.80221263600193 NA NA
## ENSMUSG00000085842 21.9171266358604 5.16454652547104 2.11409207622401 2.4429146599402 NA NA
## ... ... ... ... ... ... ...
## ENSMUSG00000005871 403.634302628017 -0.524237280126561 0.979145628125162 -0.535402768565034 NA NA
## ENSMUSG00000044595 41.1231231533217 1.83318093904902 1.6810418210932 1.09050287509021 NA NA
## ENSMUSG00000024597 346.21374732627 -1.29473000804499 0.973881588495617 -1.32945321416846 NA NA
## ENSMUSG00000118138 23.291043050614 5.90635654386202 3.5290227276766 1.67365216935018 NA NA
## ENSMUSG00000033417 290.918339858813 -0.928829346925173 0.966631349728102 -0.960893051095888 NA NA
# Low counts (only padj is NA)
res[which(is.na(res$padj) & !is.na(res$pvalue)), ]
## log2 fold change (MLE): treatment A vs E
## Wald test p-value: treatment A vs E
## DataFrame with 2840 rows and 6 columns
## baseMean log2FoldChange lfcSE stat pvalue padj
## <numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
## ENSMUSG00000025900 4.914147077455 -4.92038939858524 2.01247166836637 -2.44494840644359 0.0144872864884619 NA
## ENSMUSG00000098104 4.09533781074173 0.853116865234774 1.10703456347057 0.770632546973277 0.440924763843958 NA
## ENSMUSG00000102275 2.36352235488834 -0.391339522802433 1.46485014416748 -0.267153281419679 0.789351145202682 NA
## ENSMUSG00000102135 4.9457418399281 -0.328110499112732 1.08089829259967 -0.303553536312462 0.761468054712299 NA
## ENSMUSG00000098201 2.01346784837404 -0.896309374862552 1.72861159166932 -0.518514037035346 0.604099668891218 NA
## ... ... ... ... ... ... ...
## ENSMUSG00000064344 2.73922713299793 0.163917921084994 1.41591685542737 0.115768041362512 0.907836379470033 NA
## ENSMUSG00000064349 3.00782467550163 -0.12734120960026 1.24974955476499 -0.101893382649939 0.918841302505899 NA
## ENSMUSG00000064358 2.70134753598084 0.141492229313294 1.65446199795407 0.0855215952305128 0.93184672785224 NA
## ENSMUSG00000064369 4.23154180234235 1.42531627839752 1.24382109968624 1.14591743037409 0.251829318362109 NA
## ENSMUSG00000061654 1.69274896880504 1.42421218490796 2.57607432131434 0.552861450123578 0.58035828636321 NA
Shrunken LFC results
plotMA(res)
# Shrunken LFC results
res_shrunken <- lfcShrink(
dds_full,
coef = str_c('treatment_', condition, '_vs_', control),
type = 'apeglm'
)
res_shrunken
## log2 fold change (MAP): treatment A vs E
## Wald test p-value: treatment A vs E
## DataFrame with 14648 rows and 5 columns
## baseMean log2FoldChange lfcSE pvalue padj
## <numeric> <numeric> <numeric> <numeric> <numeric>
## ENSMUSG00000025900 4.914147077455 -0.241445095152026 0.566057167536038 0.0144872864884619 0.152194512661901
## ENSMUSG00000098104 4.09533781074173 0.140171540467965 0.461172330088417 0.440924763843958 0.746711563326519
## ENSMUSG00000033845 107.622165011027 -0.0595416997344277 0.318135895241512 0.799513919532903 0.92952733548211
## ENSMUSG00000102275 2.36352235488834 -0.0364245117406618 0.454994221479306 0.789351145202682 NA
## ENSMUSG00000025903 97.3741809067814 0.000553662408882452 0.339890271773017 0.99889876790933 0.999554374615645
## ... ... ... ... ... ...
## ENSMUSG00000061654 1.69274896880504 0.0466582594287195 0.472044184129129 0.58035828636321 NA
## ENSMUSG00000079834 28.8069677321529 0.285902217369414 0.497015110137123 0.216664518073648 0.55206916045031
## ENSMUSG00000095041 184.206277782681 0.0410310477560653 0.367468135932862 0.864473246335269 0.954844930608897
## ENSMUSG00000063897 31.5444997848201 -0.0639566027984351 0.400050017030481 0.770179788725231 0.916738662708078
## ENSMUSG00000095742 10.1107048409608 0.0313236418014735 0.425676345462796 0.874356596236297 0.958102496428203
plotMA(res_shrunken)
mcols(res_shrunken)
## DataFrame with 5 rows and 2 columns
## type description
## <character> <character>
## baseMean intermediate mean of normalized counts for all samples
## log2FoldChange results log2 fold change (MAP): treatment A vs E
## lfcSE results posterior SD: treatment A vs E
## pvalue results Wald test p-value: treatment A vs E
## padj results BH adjusted p-values
summary(res_shrunken, alpha = pval_cutoff)
##
## out of 14648 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0 (up) : 308, 2.1%
## LFC < 0 (down) : 175, 1.2%
## outliers [1] : 179, 1.2%
## low counts [2] : 2272, 16%
## (mean count < 4)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
Summary details
# Upregulated genes (LFC > 0)
res_shrunken_sig_df %>% filter(log2FoldChange > 0)
# Downregulated genes (LFC < 0)
res_shrunken_sig_df %>% filter(log2FoldChange < 0)
# Outliers (pvalue and padj are NA)
res_shrunken[which(is.na(res_shrunken$pvalue)), ]
## log2 fold change (MAP): treatment A vs E
## Wald test p-value: treatment A vs E
## DataFrame with 179 rows and 5 columns
## baseMean log2FoldChange lfcSE pvalue padj
## <numeric> <numeric> <numeric> <numeric> <numeric>
## ENSMUSG00000067780 318.83564782775 -0.22411703750797 0.536162264989718 NA NA
## ENSMUSG00000025981 152.077989616933 -0.0416595659544155 0.442086642338937 NA NA
## ENSMUSG00000038349 100.799409633029 -2.96028380309171 1.53835977480067 NA NA
## ENSMUSG00000026024 50.6697012550001 -0.55794504867263 1.14073581036123 NA NA
## ENSMUSG00000085842 21.9171266358604 0.171735351785563 0.521147556380492 NA NA
## ... ... ... ... ... ...
## ENSMUSG00000005871 403.634302628017 -0.0999653241456368 0.440350464820972 NA NA
## ENSMUSG00000044595 41.1231231533217 0.129395103559718 0.487081495732831 NA NA
## ENSMUSG00000024597 346.21374732627 -0.271004345889595 0.521619060100433 NA NA
## ENSMUSG00000118138 23.291043050614 0.0577703753824257 0.480736973051452 NA NA
## ENSMUSG00000033417 290.918339858813 -0.187614640240523 0.470764277170404 NA NA
# Low counts (only padj is NA)
res_shrunken[which(is.na(res_shrunken$padj) & !is.na(res_shrunken$pvalue)), ]
## log2 fold change (MAP): treatment A vs E
## Wald test p-value: treatment A vs E
## DataFrame with 2272 rows and 5 columns
## baseMean log2FoldChange lfcSE pvalue padj
## <numeric> <numeric> <numeric> <numeric> <numeric>
## ENSMUSG00000102275 2.36352235488834 -0.0364245117406618 0.454994221479306 0.789351145202682 NA
## ENSMUSG00000098201 2.01346784837404 -0.0631972789924058 0.464941327316382 0.604099668891218 NA
## ENSMUSG00000103903 3.35339380940403 0.0953761002836069 0.477444385207544 0.365967184382433 NA
## ENSMUSG00000079671 1.77247301755752 -0.031554464222372 0.459385323682869 0.799750938797838 NA
## ENSMUSG00000083422 2.09976591294719 -0.118324077380156 0.484283472973611 0.29066750440083 NA
## ... ... ... ... ... ...
## ENSMUSG00000064342 2.42847689971841 0.0625177992571637 0.469762522277571 0.546404966544711 NA
## ENSMUSG00000064344 2.73922713299793 0.0168427484873255 0.451689823183754 0.907836379470033 NA
## ENSMUSG00000064349 3.00782467550163 -0.0158356528673377 0.444956540472799 0.918841302505899 NA
## ENSMUSG00000064358 2.70134753598084 0.010794852575915 0.458128601167714 0.93184672785224 NA
## ENSMUSG00000061654 1.69274896880504 0.0466582594287195 0.472044184129129 0.58035828636321 NA
Visualizing results
Heatmaps
# Plot normalized counts (z-scores)
pheatmap(counts_sig_norm[2:7],
color = brewer.pal(8, 'YlOrRd'),
cluster_rows = T,
show_rownames = F,
annotation_col = as.data.frame(colData(dds)) %>% select(label),
border_color = NA,
fontsize = 10,
scale = 'row',
fontsize_row = 10,
height = 20)
# Plot log-transformed counts
pheatmap(counts_sig_log[2:7],
color = rev(brewer.pal(8, 'RdYlBu')),
cluster_rows = T,
show_rownames = F,
annotation_col = as.data.frame(colData(dds)) %>% select(label),
border_color = NA,
fontsize = 10,
fontsize_row = 10,
height = 20)
# Plot log-transformed counts (top 24 DE genes)
pheatmap((counts_sig_log %>% filter(ensembl_gene_id %in% res_sig_df$ensembl_gene_id[1:24]))[2:7],
color = rev(brewer.pal(8, 'RdYlBu')),
cluster_rows = T,
show_rownames = F,
annotation_col = as.data.frame(colData(dds)) %>% select(label),
fontsize = 10,
fontsize_row = 10,
height = 20)
Volcano plots
# Unshrunken LFC
res_df %>%
mutate(
sig_threshold = if_else(
padj < pval_cutoff & abs(log2FoldChange) >= lfc_cutoff,
if_else(log2FoldChange > 0, 'DE-up', 'DE-down'),
'non-DE'
)
) %>%
filter(!is.na(sig_threshold)) %>%
ggplot() +
geom_point(aes(x = log2FoldChange, y = -log10(padj), colour = sig_threshold)) +
scale_color_manual(values = c('blue', 'red', 'gray')) +
xlab('log2 fold change') +
ylab('-log10 adjusted p-value')
# Shrunken LFC
res_shrunken_df %>%
mutate(
sig_threshold = if_else(
padj < pval_cutoff & abs(log2FoldChange) >= lfc_cutoff,
if_else(log2FoldChange > 0, 'DE-up', 'DE-down'),
'non-DE'
)
) %>%
filter(!is.na(sig_threshold)) %>%
ggplot() +
geom_point(aes(x = log2FoldChange, y = -log10(padj), colour = sig_threshold)) +
scale_color_manual(values = c('blue', 'red', 'gray')) +
xlab('log2 fold change') +
ylab('-log10 adjusted p-value')
GSEA (all)
Hallmark genesets
# Shrunken LFC
get_fgsea_res(rank_lfc, mm_h) %>% plot_enrichment_table(rank_lfc, mm_h)
# Wald stat
get_fgsea_res(rank_stat, mm_h) %>% plot_enrichment_table(rank_stat, mm_h)
# Rank: sign(LFC) * -log10(pvalue)
get_fgsea_res(rank_pval, mm_h) %>% plot_enrichment_table(rank_pval, mm_h)
GO biological process
# Shrunken LFC
get_fgsea_res(rank_lfc, mm_c5_bp) %>% plot_enrichment_table(rank_lfc, mm_c5_bp)
# Wald stat
get_fgsea_res(rank_stat, mm_c5_bp) %>% plot_enrichment_table(rank_stat, mm_c5_bp)
# Rank: sign(LFC) * -log10(pvalue)
get_fgsea_res(rank_pval, mm_c5_bp) %>% plot_enrichment_table(rank_pval, mm_c5_bp)
GO cellular component
# Shrunken LFC
get_fgsea_res(rank_lfc, mm_c5_cc) %>% plot_enrichment_table(rank_lfc, mm_c5_cc)
# Wald stat
get_fgsea_res(rank_stat, mm_c5_cc) %>% plot_enrichment_table(rank_stat, mm_c5_cc)
# Rank: sign(LFC) * -log10(pvalue)
get_fgsea_res(rank_pval, mm_c5_cc) %>% plot_enrichment_table(rank_pval, mm_c5_cc)
GO molecular function
# Shrunken LFC
get_fgsea_res(rank_lfc, mm_c5_mf) %>% plot_enrichment_table(rank_lfc, mm_c5_mf)
# Wald stat
get_fgsea_res(rank_stat, mm_c5_mf) %>% plot_enrichment_table(rank_stat, mm_c5_mf)
# Rank: sign(LFC) * -log10(pvalue)
get_fgsea_res(rank_pval, mm_c5_mf) %>% plot_enrichment_table(rank_pval, mm_c5_mf)
GSEA (DE)
Hallmark genesets
# Shrunken LFC
get_fgsea_res(rank_lfc, mm_h) %>% plot_enrichment_table(rank_lfc, mm_h)
# Wald stat
get_fgsea_res(rank_stat, mm_h) %>% plot_enrichment_table(rank_stat, mm_h)
# Rank: sign(LFC) * -log10(pvalue)
get_fgsea_res(rank_pval, mm_h) %>% plot_enrichment_table(rank_pval, mm_h)
GO biological process
# Shrunken LFC
get_fgsea_res(rank_lfc, mm_c5_bp) %>% plot_enrichment_table(rank_lfc, mm_c5_bp)
# Wald stat
get_fgsea_res(rank_stat, mm_c5_bp) %>% plot_enrichment_table(rank_stat, mm_c5_bp)
# Rank: sign(LFC) * -log10(pvalue)
get_fgsea_res(rank_pval, mm_c5_bp) %>% plot_enrichment_table(rank_pval, mm_c5_bp)
GO cellular component
# Shrunken LFC
get_fgsea_res(rank_lfc, mm_c5_cc) %>% plot_enrichment_table(rank_lfc, mm_c5_cc)
# Wald stat
get_fgsea_res(rank_stat, mm_c5_cc) %>% plot_enrichment_table(rank_stat, mm_c5_cc)
# Rank: sign(LFC) * -log10(pvalue)
get_fgsea_res(rank_pval, mm_c5_cc) %>% plot_enrichment_table(rank_pval, mm_c5_cc)
GO molecular function
# Shrunken LFC
get_fgsea_res(rank_lfc, mm_c5_mf) %>% plot_enrichment_table(rank_lfc, mm_c5_mf)
# Wald stat
get_fgsea_res(rank_stat, mm_c5_mf) %>% plot_enrichment_table(rank_stat, mm_c5_mf)
# Rank: sign(LFC) * -log10(pvalue)
get_fgsea_res(rank_pval, mm_c5_mf) %>% plot_enrichment_table(rank_pval, mm_c5_mf)
System Info
sessionInfo()
## R version 3.6.3 (2020-02-29)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Sierra 10.12.6
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] fgsea_1.12.0 Rcpp_1.0.3 RColorBrewer_1.1-2 pheatmap_1.0.12 DESeq2_1.26.0 SummarizedExperiment_1.16.1 DelayedArray_0.12.3 BiocParallel_1.20.1 matrixStats_0.57.0 Biobase_2.46.0 GenomicRanges_1.38.0 GenomeInfoDb_1.22.1 IRanges_2.20.2 S4Vectors_0.24.4 BiocGenerics_0.32.0 scales_1.1.1 forcats_0.4.0 stringr_1.4.0 dplyr_1.0.2 purrr_0.3.3 readr_1.3.1 tidyr_1.0.0 tibble_3.1.0 ggplot2_3.3.3 tidyverse_1.2.1
##
## loaded via a namespace (and not attached):
## [1] colorspace_1.4-1 ellipsis_0.3.0 htmlTable_1.13.3 XVector_0.26.0 base64enc_0.1-3 rstudioapi_0.10 farver_2.1.0 bit64_0.9-7 mvtnorm_1.1-1 apeglm_1.8.0 AnnotationDbi_1.48.0 fansi_0.4.0 lubridate_1.7.4 xml2_1.2.2 splines_3.6.3 geneplotter_1.64.0 knitr_1.25 Formula_1.2-3 jsonlite_1.6 broom_0.7.5 annotate_1.64.0 cluster_2.1.0 png_0.1-7 compiler_3.6.3 httr_1.4.1 backports_1.1.5 assertthat_0.2.1 Matrix_1.2-18 cli_1.1.0 acepack_1.4.1 htmltools_0.5.1.1 tools_3.6.3 coda_0.19-3 gtable_0.3.0 glue_1.4.2 GenomeInfoDbData_1.2.2 fastmatch_1.1-0 bbmle_1.0.23.1 cellranger_1.1.0 jquerylib_0.1.3 vctrs_0.3.4 xfun_0.22 rvest_0.3.5 lifecycle_0.2.0 XML_3.99-0.3 MASS_7.3-51.5 zlibbioc_1.32.0 hms_0.5.2 yaml_2.2.0 memoise_1.1.0 gridExtra_2.3 emdbook_1.3.12 sass_0.3.1 bdsmatrix_1.3-4 rpart_4.1-15 latticeExtra_0.6-29 stringi_1.4.3 RSQLite_2.2.1 genefilter_1.68.0 checkmate_1.9.4 rlang_0.4.8 pkgconfig_2.0.3 bitops_1.0-6 evaluate_0.14 lattice_0.20-38 labeling_0.3 htmlwidgets_1.5.1 bit_1.1-15.1 tidyselect_1.1.0 plyr_1.8.4 magrittr_1.5 R6_2.4.0 generics_0.0.2 Hmisc_4.3-0 DBI_1.1.0 pillar_1.5.1 haven_2.2.0 foreign_0.8-75 withr_2.1.2 survival_3.1-8 RCurl_1.95-4.12 nnet_7.3-12 modelr_0.1.5 crayon_1.3.4 utf8_1.1.4 rmarkdown_2.7 jpeg_0.1-8.1 locfit_1.5-9.4 grid_3.6.3 readxl_1.3.1 data.table_1.13.6 blob_1.2.1 digest_0.6.27 xtable_1.8-4 numDeriv_2016.8-1.1 munsell_0.5.0 bslib_0.2.4